Dimensional Direction Of Arrival Research Articles

Generalized parallel coprime array for two-dimensional DOA estimation: A perspective from maximizing degree of freedom

Parallel arrays with coprime subarrays have shown its potential advantages for two dimensional direction of arrival (DOA) estimation. In this paper, by introducing two flexible coprime factors to enlarge the inter-element spacing of parallel uniform subarrays, we propose a generalized parallel coprime array (GPCA) geometry. The proposed geometry enjoys flexible array layouts by the coprime factors and enables to extend the array aperture to achieve great improvement of estimation performance. Meanwhile, we verify that GPCA always can obtain M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> degrees of freedom (DOFs) in co-array domain via 2M sensors after optimization, which outperforms sparse parallel array geometries, such as parallel coprime array (PCA) and parallel augmented coprime array (PACA), and is the same as parallel nested array (PNA) with extended aperture. The superiority of GPCA geometry has been proved by numerical simulations with sparse representation methods.

Improved Method of Direction Finding for Non Circular Signals with Wavelet Denoising Using Three Parallel Uniform Linear Arrays

In this paper, two dimensional direction of arrival (2D-DOA) estimation of non circular signals using three parallel uniform linear arrays is addressed. Wavelet denoising is used as a preprocessing step to improve signal to noise ratio (SNR) of the received noise corrupted signal. Gain improved signal is then used for the 2D angle estimation. In this proposed work, we consider non circular signals where the real and imaginary part of received signal is not a independent random variable, so the imaginary part of the signals is also included, which resembles the real time situation. Thus, the array aperture is enlarged to get improved performance in angle estimation. The simulation results are compared with the existing literature of three parallel uniform linear arrays. The simulation results and analysis show that the proposed method has high accuracy and significant performance. Cramer Rao lower bound has been given for the reference.

Two-Step Sparse Representation Based 2D DOA Estimation with Single Moving Acoustic Vector Sensor

This paper investigates the two dimensional direction of arrival (2D DOA) estimation problem of multiple sources with one single moving acoustic vector sensor (AVS). We first use one single moving AVS to construct a synthetic nested AVS array, which is later shown that is equivalent to the physical nested AVS array. Then the vectorization and row extraction operations are performed to obtain the observation vector that behaves like signals received by a virtual uniform AVS array. Finally, the 2D DOA estimation is obtained via a two-step sparse representation (SR) method, which transforms the 2D grid search to a computationally efficient 1D grid search. The Cramer-Rao bound comparison between the synthetic and physical nested AVS arrays shows that these two arrays are equivalent for DOA estimation. Based on the property of the nested arrays and the full utilization of the array aperture via SR, the proposed method can achieve better estimation performance than spatial smoothing methods with nested AVS arrays and methods with uniform AVS arrays. Simulations validate the effectiveness of the proposed synthetic array method.

2D-DOA Estimation Based on MSCS of Multi-FH Signal

A two dimensional direction of arrival (2D-DOA) estimation algorithm based on MSCS is proposed by using space-time-frequency analyzing method. Firstly, the spatial time-frequency (TF) matrix of each hop is constructed by using the time-frequency domain features of FH signals; Then the dimension of the noise subspace is descended by the singular value decomposition on the intersection of noise subspace and its conjugate one; Finally efficient DOA estimation is realized through half-spectrum searching. Theoretical analysis and simulation results show that this algorithm has good effectiveness and estimation performance.

LPA 2D-DOA Estimation for Fast Nonstationary Sources Using New Array Geometry Configuration

This paper proposes a new array geometry configuration to improve the two dimensional direction of arrival (2D-DOA) estimation of narrowband moving sources with less complexity. This new array is denoted by verticircular configuration, which is composed of both Uniform linear array (ULA) and Uniform Circular array (UCA) to avoid too much computation for 2D-DOA estimation. The proposed verticircular array is applied with the LPA nonparametric estimator to estimate multiple rapidly moving sources’ parameters (angles and angular velocities) for both azimuth as well as elevation directions. Simulation results show that this nonparametric technique is capable of resolving closely spaced sources provided that their velocities are sufficiently different with decreased computational complexity when using the verticircular array. Different scenarios are used to show the efficient LPA beamformer to distinguish sources that can have the same angles using their different angular velocities. In addition, this paper is to compare the performance of the 2DLPA DOA estimation algorithm when using verticircular array (proposed array geometry) or rectangular planar array geometry. Simulation results show that the performance of the proposed method with less complexity than that obtained when using rectangular planar array.

Nested Arrays in Two Dimensions, Part I: Geometrical Considerations

A new class of two dimensional arrays with sensors on lattice(s) is proposed, whose difference co-array can give rise to a virtual two dimensional array with much larger number of elements on a “dense” lattice. This structure is obtained by systematically nesting two arrays, one with sensors on a sparse lattice and the other on a dense lattice where the lattices bear a certain relation with each other. The difference co-array of such an array with M and N elements respectively on the two lattices, is a two dimensional array with O(MN) elements present contiguously (without holes) on the dense lattice. The difference co-array can be realized on any arbitrary lattice by choosing the dense and sparse lattices appropriately. The generator matrices of the sparse and the dense lattices are related by an integer matrix. The Smith form of this integer matrix is shown to provide a very insightful perspective which is exploited heavily in the construction of nested arrays. The design of the two dimensional nested array gives rise to several interesting geometrical orientations of the co-array which are addressed in detail, and it is shown how the orientations can be manipulated to yield more virtual sensors in a continuum on the dense lattice. The increased number of elements in the virtual difference co-array can be exploited to perform two dimensional direction of arrival (DOA) estimation of many more sources than what traditional methods can achieve. A novel algorithm for application of the nested array in two dimensional direction of arrival estimation is reported in the accompanying part II of the paper.

Trilinear Decomposition-Based Two-Dimensional DOA Estimation Algorithm for Arbitrarily Spaced Acoustic Vector-Sensor Array Subjected to Unknown Locations

This paper links the acoustic vector-sensor array parameter estimation problem to the trilinear model. We derive a blind two dimensional direction of arrival (DOA) estimation algorithm for arbitrarily spaced acoustic vector-sensor array at unknown location when exploiting the approach of trilinear decomposition. We present a novel method which illustrates better DOA estimation performance compared to ESPRIT algorithm. Furthermore, our algorithm requires no spectral peak searching or pair matching. Numerical results demonstrate the validity of our algorithm.

A modular neural network for direction-of-arrival estimation of two sources

This work addresses the problem of estimating the direction-of-arrival (DOA) of two sources using an array of sensors. This problem is mostly useful in radar applications, where we have few targets at each range bin. Super-resolution algorithms, such as maximum likelihood (ML) estimation and multiple signal classification (MUSIC), have been applied to this problem, but the former involves high computation efforts, while the later has poor estimation performance for coherent sources. In this work, we propose a DOA estimation network, named RBF-AML, which combines the approximated ML (AML) estimator and a radial basis function (RBF) neural network (NN). In the proposed RBF-AML network, the entire two dimensional DOA space is divided into multiple sectors covered by RBF experts. The AML function is then used as a mediator among the experts and selects the most suitable one as the final output of the system. The performance of the RBF-AML network for a two coherent sources case in a Y shape array configuration is evaluated. We show that the performance of the RBF-AML network is similar to the performance of the classical AML DOA estimation for various signal-to-noise ratios (SNRs), phase of the correlation coefficient and signal-to-interference ratios (SIRs). Furthermore, the RBF-AML network requires fewer computational efforts than the classical AML DOA estimation and therefore is an attractive choice for real-time applications.