Enhanced DOA Estimation for MIMO radar in the Case of Limited Snapshots

Multiple-input-multiple-output (MIMO) radar is
well-known for providing high-resolution direction-of-arrival
(DOA) estimation by forming a large-scaled sum coarray utilizing
waveform diversity. However, the sacrifice is that a large number
of snapshots are required to estimate the sample covariance
matrix. When the number of training snapshots is limited,
the performance of subspace-based DOA estimation method,
such as multiple signal classification (MUSIC), deteriorates due
to the distortion of noise subspace. In order to improve the
accuracy of DOA estimation using MIMO radar in the case of
few snapshots, we propose a method to refine the covariance
matrix iteratively. The sampled covariance matrix is iteratively
refined by subtracting cross-correlation terms using generalized
inner product based on the previous DOA estimates. Finally,
the MUSIC algorithm is implemented based on the refined
sample covariance matrix to update the DOA estimates until
achieving termination condition. Simulation results demonstrate
that the additional covariance matrix refinement step enhances
the accuracy of DOA estimation using MIMO radar in the case
of limited snapshots significantly.