Extension of Nested Arrays with the Fourth-Order Difference Co-Array Enhancement

To reach a higher number of degrees of freedom by exploiting the fourth-order difference co-array concept, an effective structure extension based on two-level nested arrays is proposed. It increases the number of consecutive lags in the fourth-order difference coarray, and a virtual uniform linear array (ULA) with more sensors and a larger aperture is then generated from the proposed structure, leading to a much higher number of distinguishable sources with a higher accuracy. Compressive sensing based approach is applied for direction-of-arrival (DOA) estimation by vectorizing the fourthorder cumulant matrix of the array, assuming non-Gaussian impinging signals

Extension of Co-Prime Arrays Based on the Fourth-Order Difference Co-Array Concept

An effective sparse array extension method for maximizing the number of consecutive lags in the fourth-order difference co-array is proposed, leading to a novel enhanced sparse array structure based on co-prime arrays (CPAs) with significantly increased number of degrees of freedom (DOFs). One method to exploit the increased DOFs based on nonstationary signals is also proposed, with simulation results provided to demonstrate the effectiveness of the proposed structure

Displaced thinned coprime arrays with an additional sensor for DOA estimation

A new sparse array structure based on the recently proposed thinned coprime arrays is proposed to maximize the number of unique lags. The design process involves two stages: the first stage displaces one subarray from its original position for an increase in the number of lags; as the displacement results in the minimum interelement spacing equal to integer multiples of half-wavelength, an additional sensor at a distance of half-wavelength is then added in the displaced subarray to avoid spatial aliasing. The strategic location of the additional sensor results in a significant increase in the overall unique lags which can be utilized for direction-of-arrival estimation (DOA) using compressive sensing based methods. Furthermore, the new structure has excellent performance in the presence of mutual coupling as shown by simulation results

Wideband DOA Estimation with Frequency Decomposition via a Unified GS-WSpSF Framework

A unified group sparsity based framework for wideband sparse spectrum fitting (GS-WSpSF) is proposed for wideband direction-of-arrival (DOA) estimation, which is capable of handling both uncorrelated and correlated sources. Then, by making four different assumptions on a priori knowledge about the sources, four variants under the proposed framework are formulated as solutions to the underdetermined DOA estimation problem without the need of employing sparse arrays. As verified by simulations, improved estimation performance can be achieved by the wideband methods compared with narrowband ones, and adopting more a priori information leads to better performance in terms of resolution capacity and estimation accuracy