Recently, dilated nested arrays have been proposed on a moving platform to increase the uniform degrees of freedom (uDOF) by a factor of three by exploiting array motion. However, no literature addresses the issue whether the same dilation method still performs well for other array geometries such as coprime arrays, augmented nested arrays and minimum redundancy arrays. Compared with nested arrays, these arrays either achieve higher uDOF or exhibit more robustness to mutual coupling among sensors. In this paper, we propose a novel sparse array geometry named dilated arrays (DAs) on a moving platform by applying the dilation method to other array geometries. First, by exploiting the relationship between the element positions in the difference coarrays of the original linear array and the synthetic array after motion, we prove that, for a DA on a moving platform, the maximum uDOF can be tripled compared to that of its original array regardless of the array geometry. Therefore, the number of sources that can be resolved for direction-of-arrival (DOA) estimation is increased threefold. Second, we prove that a DA reduces mutual coupling compared with its original array. As a result, the DA is more robust to mutual coupling than its original array. Third, we extend one-dimensional DAs to the two-dimensional (2-D) case, yielding a new 2-D sparse array geometry named two-parallel DAs. We show that by exploiting array motion, two-parallel DAs can increase the number of detectable sources threefold. Numerical simulations demonstrate the superior performance of the proposed array geometries.

In this letter, a novel approach to construct virtual array is proposed by exploiting synthetic aperture technology and the concept of difference coarray for sparse arrays. First, we prove that the proposed method can increase the degrees of freedom (DOF) of an arbitrary moving sparse array threefold. Therefore, the maximal number of resolvable sources for direction-of-arrival (DOA) estimation is tripled as well. Furthermore, we prove that the difference coarray of synthetic arrays is a hole-free uniform linear array (ULA) if the difference coarray of original arrays is hole-free. Therefore, for ULA based DOA estimation methods, better performance can be achieved by fully employing all elements in the difference coarray of synthetic arrays. In addition, unlike the dilated nested array method, which enlarges the intersensor spacing of nested arrays threefold to triple DOF, the proposed method does not need to increase array aperture physically. Thus it is more practical to mount a sensor array on a moving platform when taking space limitation into consideration. Simulation results are given to demonstrate the superiority of the proposed method.

A novel direction-of-arrival (DOA) estimation method is proposed based on the sparse cumulants fitting without redundancy. Firstly, we derive that some fourth order cumulants of the array output are redundant and therefore are removed to reduce computational complexity. Then, the left cumulants are sparsely represented on an overcomplete basis and the DOAs are resolved by using a software package. Despite introducing a high variance, the proposed method shows several advantages including the ability to detect more sources than sensors, high resolution, and robustness to all kinds of Gaussian noise. Besides, our method does not have to know, a priori, the number of sources. Simulation results are presented to illustrate the effectiveness and efficiency of the proposed method.

In this paper, a new sensor array geometry, called a compressed symmetric nested array (CSNA), is designed to increase the degrees of freedom in the near field. As its name suggests, a CSNA is constructed by getting rid of some elements from two identical nested arrays. The closed form expressions are also presented for the sensor locations and the largest degrees of freedom obtainable as a function of the total number of sensors. Furthermore, a novel DOA estimation method is proposed by utilizing the CSNA in the near field. By employing this new array geometry, our method can identify more sources than sensors. Compared with other existing methods, the proposed method achieves higher resolution because of increased array aperture. Simulation results are demonstrated to verify the effectiveness of the proposed method.

For $q$ -dilated arrays, i.e., dilated arrays with dilation factor $q$ , the uniform degrees of freedom (uDOFs) of the synthetic array after array motion can be increased by a factor of $q$ compared with that of original linear arrays for $q\leq 3$ . However, when $q\geq 4$ , the number of uDOFs of the synthetic arrays of $q$ -dilated arrays is only three using existing moving array models. To achieve a high number of uDOFs for $q$ -dilated arrays with $q\geq 4$ , in this article, we design a new moving array processing model in $q$ -dilated arrays, which synthesizes multiple shifted arrays with displacements of half wavelength multiples. The number of shifted arrays in the proposed model is shown to be a function of the dilation factor $q$ . First, we prove that the number of uDOFs of synthetic arrays of $q$ -dilated arrays can be $q $ times that of their original arrays for arbitrary positive integer $q$ . Hence, the maximum number of detectable sources for direction-of-arrival (DOA) estimation is increased by a factor of $q $ . Second, we apply the new model to two-parallel $q$ -dilated arrays to estimate the 2-D DOAs, increasing the number of identifiable sources by a factor of $q$ for 2-D DOA estimation. Numerical examples show the superiority of the proposed model based on $q $ -dilated arrays.

Source localization using sensor array in the near-field is a two-dimensional nonlinear parameter estimation problem which requires jointly estimating the two parameters: direction-of-arrival and range. In this paper, a new source localization method based on sparse signal reconstruction is proposed in the near-field. We first utilize l1-regularized weighted least-squares to find the bearings of sources. Here, the weight is designed by making use of the probability distribution of spatial correlations among symmetric sensors of the array. Meanwhile, a theoretical guidance for choosing a proper regularization parameter is also presented. Then one well-known l1-norm optimization solver is employed to estimate the ranges. The proposed method has a lower variance and higher resolution compared with other methods. Simulation results are given to demonstrate the superior performance of the proposed method.