  This paper focuses on the gridless direction-of-arrival (DoA) estimation for
data acquired by non-uniform linear arrays (NLAs) in automotive applications.
Atomic norm minimization (ANM) is a promising gridless sparse recovery
algorithm under the Toeplitz model and solved by convex relaxation, thus it is
only applicable to uniform linear arrays (ULAs) with array manifolds having a
Vandermonde structure. In automotive applications, it is essential to apply the
gridless DoA estimation to NLAs with arbitrary geometry with efficiency. In
this paper, a fast ANM-based gridless DoA estimation algorithm for NLAs is
proposed, which employs the array manifold separation technique and the
accelerated proximal gradient (APG) technique, making it applicable to NLAs
without losing of efficiency. Simulation and measurement experiments on
automotive multiple-input multiple-output (MIMO) radars demonstrate the
superiority of the proposed method.
