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

Abstract

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.

A dilating and embedding scheme for constructing sparse MIMO radar array

“…Simulation results: To verify the performance of the DMISC-ETCA, this section simulates uDOFs and mutual coupling leakage versus the number of sensors, and the root mean square error (RMSE) [14] of the DMISC-ETCA, NNMIMO [6], NGMIMO [8], and NTRMIMO [7] versus different SNRs and a number of snapshots. The DOA estimation is realized using the spatial smoothing MUSIC algorithm [15].…”