Robust space‐time adaptive processing for airborne radar with coprime arrays in presence of gain and phase errors

Abstract

Space‐time adaptive processing (STAP) for sparse arrays such as coprime and nested arrays is shown to have improved performance for clutter suppression in airborne radar as compared with uniform linear arrays with the same size. However, most of the existing STAP algorithms are derived based on the assumption that the array manifold is exactly known. In this study, a robust STAP algorithm for the clutter suppression with coprime arrays in the presence of gain and phase (GP) errors is proposed. In particular, the virtual space‐time signal with GP errors is first formulated using the covariance matrix of the perturbed array received data under the Gaussian distributed interference assumption. Then, the problem of the clutter spectrum and GP errors' estimates is formulated as a joint minimization optimization problem by exploiting the sparsity of the clutter in the angle–Doppler plane. Finally, a two‐step approach based on the sparse recovery and least‐squared method is developed to solve the resultant optimization problem and the STAP filter can be designed using the clutter spectrum and GP errors' estimates. Compared with the existing sparsity‐based STAP algorithms for coprime arrays, the proposed algorithm shows more robustness to GP errors as illustrated by simulation results.

A Novel Fast Iterative STAP Method with a Coprime Sampling Structure

“…Common sparse arrays include minimal redundancy arrays [ 23 , 24 ], nested arrays [ 5 , 25 , 26 , 27 ], and coprime arrays [ 5 , 28 , 29 , 30 , 31 ]. Minimally redundant arrays provide the largest contiguous array for a given number of sensors, but lack closed expressions for sensor locations and DOF.…”

Coarray Polarization Smoothing for DOA Estimation with Coprime Vector Sensor Arrays

“…Over the past decade, coprime array as one of the structured nonuniform sparse arrays has been widely applied in solving various sensor array processing problems. Details may refer to [1]- [35]. Among of these references, the concept of the coprime array is first established in [1], where the idea of difference coarray for improving the degrees-of-freedom (DOFs) in direction-of-arrival (DOA) estimation is also introduced.…”

“…Among of these references, the concept of the coprime array is first established in [1], where the idea of difference coarray for improving the degrees-of-freedom (DOFs) in direction-of-arrival (DOA) estimation is also introduced. Since then, the core idea of coprime array is implemented for one-dimensional DOA estimation [2]- [20], two-dimensional DOA estimation [21]- [24], near-field source localization [25], source number detection [26], passive beamforming [27]- [33], and space-time adaptive processing [34], [35], respectively. Conceptually, a coprime array consists of two uniform linear subarrays with both sensor number and intersensor spacing being a pair of mutual prime integers.…”