Abstract
Recent advance on signal processing has witnessed increasing interest in machine learning. In this paper, we revisit the problem of direction-of-arrival (DOA) estimation for colocated multiple-input multiple-output (MIMO) radar from the perspective of machine learning. The reduced-complexity transformation is first applied on the array data from matched filters, thus eliminating the redundancy of the array data for the relief of calculational burden. Furthermore, the pre-whitening is followed to obtain a simplified noise model. Finally, the DOA estimation is linked to off-grid sparse Bayesian learning (OGSBL), which does not require to update the noise hyper-parameter, and a block hyper-parameter is utilized to accelerate the convergence of the OGSBL algorithm. The proposed estimator provides better DOA estimation accuracy than the existing peak searching algorithm. The effectiveness of the proposed algorithm is verified via numerical simulation.
Highlights
The past decade has witnessed an explosive growth in machine learning
Several RC algorithms have been developed for DOA estimation in monostatic multiple-input multiple-output (MIMO) radar, e.g., RC-ESPRIT [33], RC-multiple signal classification (MUSIC) [34] and RC-based optimization method [35]
We consider a monostatic MIMO radar system, which is configured with M transmit antennas and N receive antennas, both of which are assumed to be uniform linear array (ULA) with half-wavelength spacing
Summary
The past decade has witnessed an explosive growth in machine learning. As one of the most important subset of artificial intelligence, machine learning enables the computer system to perform a specific task efficiently, while only patterns and inference are utilized. T. Liu et al.: Off-Grid DOA Estimation for Colocated MIMO Radar via Reduced-Complexity Sparse Bayesian Learning. An iterative framework is proposed in [28], which is suitable to solve the off-grid DOA estimation problem form the one-bit measurement. Several RC algorithms have been developed for DOA estimation in monostatic MIMO radar, e.g., RC-ESPRIT [33], RC-MUSIC [34] and RC-based optimization method [35]. We revisit DOA estimation problem in colocated MIMO radar, and a novel RC-OGSBL estimator is proposed. Compared with the OGSBL algorithm in [27], the proposed algorithm has the following advantages: a) It has much lower complexity as the redundancy of the measurement has been removed via the RC transformation.
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