Doubly-Toeplitz-Based Interpolation for Joint DoA-Range Estimation Using Coprime FDA

Nowadays, sparse arrays have been a focus for direction‐of‐arrival (DOA). The existing arrays can achieve high degree of freedom (DOF) bigger than the number of sensors when using the spatial smoothing methods. However, the small inter‐element spacing degrades the DOA estimation accuracy when facing severe mutual coupling. In order to alleviate mutual coupling, a coprime linear array using three uniform linear arrays (CLA‐U3) is proposed. The expression of sensor locations is given, and the analysis of DOF is considered. The minimum subarray inter‐element spacing in CLA‐U3 is bigger than that of the existing arrays, which means that CLA‐U3 can be less sensitive to mutual coupling. Moreover, the difference co‐subarrays can fill each other's holes, so CLA‐U3 and other sparse arrays composed of three subarrays have close DOF. Simulation experiments prove the favourable performance of DOA estimation.

Section: Introductionmentioning

confidence: 99%

Direction‐of‐arrival estimation in coprime linear array using three uniform linear arrays considering mutual coupling

Zhang,

Xu,

et al. 2024

“…Sparse linear arrays (SLAs), which can offer a higher number of uniform degrees of freedom (uDOFs) for sources detection and large IES, have aroused considerable attention. To achieve under-determined DOA estimation using sparse arrays, many algorithms, such as spatial smoothing subspace MUSIC (SS-MUSIC) [17] and sparse-representation algorithms [18][19][20][21][22], have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Hole‐free sparse array for highly efficient direction of arrival estimation: Enhanced uniform difference co‐array

Zhang,

Liu,

et al. 2023

Sparse arrays with sensors can offer number of degrees of freedom by exploiting the difference co‐array that can be obtained from the second‐order statistics of received signals. In this article, a novel sparse array with seven subarrays (SA‐7S) by carefully assembling the subarrays is proposed, and the resulting SA‐7S can generate a hole‐free uniform difference co‐array. The sensor locations of the SA‐7S array are uniquely determined by a closed‐form expression and the number of achievable degrees of freedom is analytically presented as well. Theoretical analysis shows that the SA‐7S can provide a higher number of degrees of freedom than most sparse linear arrays. Simulations are performed to validate the advantage of the proposed array in terms of uDOFs, mutual coupling and direction of arrival estimation performance.

“…Many works, such as the complementary CPA [18], the padded CPA [19], the co-prime array with a filled DCA [20], and the hole-free CPA [21], were presented to partially or completely fill holes to increase the DOFs. To achieve under-determined DOA estimation using sparse arrays, many algorithms, such as spatial smoothing subspace MUSIC (SS-MUSIC) [22] and sparse-representationbased algorithms [23][24][25][26][27], have been proposed.…”

Section: Introductionmentioning

confidence: 99%

A novel sparse linear array based on fourth‐order cumulants for improved direction‐of‐arrival estimation

Mei¹,

Wang²,

Cui³

et al. 2022

Owing to increased degrees of freedom (DOFs) and reduced mutual coupling effect, sparse linear arrays can improve the performance of direction‐of‐arrival estimation compared with uniform linear arrays. In this study, a novel sparse linear array is presented based on fourth‐order cumulants, which consists of five sparse sub‐arrays. The sensor locations of the five sub‐arrays are determined by a closed‐form expression. Closed‐form expressions of DOFs for the proposed array with any number of sensors are also derived. By optimising the allocation of sensors in each sub‐array, the DOFs of the proposed array with M sensors achieve scriptO()M4 $\mathcal{O}\left({M}^{4}\right)$. Because each sub‐array is sparse, the number of pairwise sensors with small separation is significantly alleviated. Theoretical analysis shows that the proposed array can provide a higher number of DOFs and is more robust in scenarios with heavy levels of mutual coupling than most sparse linear arrays. Numerical simulations are conducted to demonstrate the merits of the proposed array over other sparse linear arrays.