Direction-of-Arrival Estimation for CS-MIMO Radar Using Subspace Sparse Bayesian Learning

Abstract

We address the problem of direction-of-arrival (DOA) estimation for compressive sensing based multiple-input multiple-output (CS-MIMO) radar. The spatial sparsity of the targets enables CS to be desirable for DOA estimation. By discretizing the possible target angles, a overcomplete dictionary is constructed for DOA estimation. A structural sparsity Bayesian learning framework is presented for support recovery. To improve the recovery accuracy and speed up the Bayesian iteration, a subspace sparse Bayesian learning algorithm is developed. The proposed scheme, which needs less iteration steps, can provides high precision DOA estimation performance for CS-MIMO radar, even at the condition of low signal-to-noise ratio and coherent sources. Simulation results verify the usefulness of our scheme.

Transfer learning‐based radar imaging with deep convolutional neural networks for distributed frequency modulated continuous waveform multiple‐input multiple‐output radars

“…In addition, when the FMCW MIMO radars are spatially distributed, each radar should perform the subspace estimation, which is not desirable for radars with limited computing resources. To avoid the subspace estimation, compressive sensing (CS) based approaches have been investigated [10][11][12][13][14][15]. In [10], the reweighted L1 minimisation method is applied to radar imaging with a single antenna FMCW radar.…”

“…In [10], the reweighted L1 minimisation method is applied to radar imaging with a single antenna FMCW radar. In [11], greedy reconstruction algorithms such as orthogonal matching pursuit (OMP) or subspace pursuit are exploited to achieve the radar images, and in [12,13], a sparse Bayesian learning algorithm is applied in the angle-of-arrival estimation. In [14,15], sparse Bayesian pursuit has been applied to pulse-Doppler MIMO radar imaging and synthetic aperture radar imaging.…”

“…the subspace estimation, compressive sensing (CS) based approaches have been investigated [10][11][12][13][14][15]. In [10], the reweighted L1 minimisation method is applied to radar imaging with a single antenna FMCW radar.…”