Abstract
The direction of arrival (DOA) estimation problem as an essential problem in the radar system is important in radar applications. In this paper, considering a multiple-input and multiple-out (MIMO) radar system, the DOA estimation problem is investigated in the scenario with fast-moving targets. The system model is first formulated, and then by exploiting both the target sparsity in the spatial domain and the temporal correlation of the moving targets, a sparse Bayesian learning (SBL)-based DOA estimation method combined with the Kalman filter (KF) is proposed. Moreover, the performances of traditional sparse-based methods are limited by the off-grid issue, and Taylor-expansion off-grid methods also have high computational complexity and limited performance. The proposed method breaks through the off-grid limit by transforming the problem in the spatial domain to that in the time domain using the movement feature. Simulation results show that the proposed method outperforms the existing methods in the DOA estimation problem for the fast-moving targets.
Highlights
Multiple-input and multiple-out (MIMO) radar systems [1,2,3] transmit orthogonal waveforms, and the corresponding matched filters are used in the receiver to distinguish the orthogonal waveforms.the virtual aperture is provided in the MIMO radar system and improves the radar performance in the target detection, estimation, and tracking [4,5]
The simulation results are given to show the performance of the proposed method in the direction of arrival (DOA) estimation
The DOA estimation problem for the colocated MIMO radar has been investigated in this paper
Summary
Multiple-input and multiple-out (MIMO) radar systems [1,2,3] transmit orthogonal waveforms, and the corresponding matched filters are used in the receiver to distinguish the orthogonal waveforms.the virtual aperture is provided in the MIMO radar system and improves the radar performance in the target detection, estimation, and tracking [4,5]. Multiple-input and multiple-out (MIMO) radar systems [1,2,3] transmit orthogonal waveforms, and the corresponding matched filters are used in the receiver to distinguish the orthogonal waveforms. The MIMO radar systems usually have two types: the colocated MIMO radar with the distance between antennas being comparable with the wavelength [6,7], and the distributed MIMO radar having larger distance between antennas [8,9,10]. Compared with the distributed MIMO radar, the colocated MIMO radar has better characteristics in terms of system synchronization and waveform diversity [11]. MIMO radar is used to estimate the direction of arrival (DOA). In the DOA estimation problem, many methods have been proposed for decades [12,13]
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