A single triangular SS-EMVS aided high-accuracy DOA estimation using a multi-scale L-shaped sparse array

Abstract

We propose a new array configuration composed of multi-scale scalar arrays and a single triangular spatially spread electromagnetic-vector-sensor (SS-EMVS) for high-accuracy two-dimensional (2D) direction-of-arrival (DOA) estimation. Two scalar arrays are placed along x-axis and y-axis, respectively, each array consists of two uniform linear arrays (ULAs), and these two ULAs have different inter-element spacings. In this manner, these two scalar arrays form a multi-scale L-shaped array. The two arms of this L-shaped scalar array are connected by a six-component SS-EMVS, which is composed of a spatially spread dipole-triad plus a spatially spread loop-triad. All the inter-element spacings in our proposed array can be larger than a half-wavelength of the incident source, thus to form a sparse array to mitigate the mutual coupling across antennas. In the proposed DOA estimation algorithm, we perform the vector-cross-product algorithm to the SS-EMVS to obtain a set of low-accuracy but unambiguous direction cosine estimation as a reference; we then impose estimation of signal parameters via rotation invariant technique (ESPRIT) algorithm to the two scalar arrays to get two sets of high-accuracy but cyclically ambiguous direction cosine estimations. Finally, the coarse estimation is used to disambiguate the fine but ambiguous estimations progressively and therefore a multiple-order disambiguation algorithm is developed. The proposed array enjoys the superiority of low redundancy and low mutual coupling. Moreover, the thresholds of the inter-sensor spacings utilized in the proposed array are also analyzed. Simulation results validate the performance of the proposed array geometry.

A Velocity-independent DOA Estimator of Underwater Acoustic Signals via an Arbitrary Cross-linear Nested Array

“…For a single signal, the algorithm has completed DOA estimation. However, in the case of multiple signals in the channel, we need to add some conditions to Equation (17) to complete their pairing. For the receiver sensors, the acoustic velocity received by each of them is similar (from 1450m/s to 1550m/s).…”

“…[16], Van et al used a sparse array in underwater acoustic DOA estimation and combined it with MUSIC algorithm to improve the accuracy of underwater DOA estimation. In references [17,18,19], researchers realized two-dimensional DOA estimation by taking advantage of the special geometric properties of the L-shaped or the parallel arrays. In particular, the researchers of [17] and [18] used the sparse arrays, which greatly improved the accuracy of DOA estimation.…”

“…In references [17,18,19], researchers realized two-dimensional DOA estimation by taking advantage of the special geometric properties of the L-shaped or the parallel arrays. In particular, the researchers of [17] and [18] used the sparse arrays, which greatly improved the accuracy of DOA estimation. While, these algorithms developed above cannot be directly used for DOA estimation in the underwater environment, and they cannot deal with the systematic influence of acoustic velocity error in underwater environments.…”

Velocity-independent and low-complexity method for 1D DOA estimation using an arbitrary cross-linear array

“…Next, it uses this result to correct the data covariance matrix and utilizes the modified covariance matrix to estimate the new DOA angle; cyclic iteration is then applied until the convergence condition is satisfied. Onedimensional DOA estimation with different structured arrays, such as L-shaped arrays [8][9][10] and a uniform rectangular array, has captured a remarkable amount of attention. The MUSIC algorithm has also aroused notable research interest [11][12][13][14] given that it has a high resolution, estimation accuracy, and stability under certain conditions.…”