Abstract
In recent years, the development of compressed sensing (CS) and array signal processing provides us with a broader perspective of 3D imaging. The CS-based imaging algorithms have a better performance than traditional methods. In addition, the sparse array can overcome the limitation of aperture size and number of antennas. Since the signal to be reconstructed is sparse for air targets, many CS-based imaging algorithms using a sparse array are proposed. However, most of those algorithms assume that the scatterers are exactly located at the pre-discretized grids, which will not hold in real scene. Aiming at finding an accurate solution to off-grid target imaging, we propose an off-grid 3D imaging method based on improved sparse Bayesian learning (SBL). Besides, the Bayesian Cramér-Rao Bound (BCRB) for off-grid bias estimator is provided. Different from previous algorithms, the proposed algorithm adopts a three-stage hierarchical sparse prior to introduce more degrees of freedom. Then variational expectation maximization method is applied to solve the sparse recovery problem through iteration, during each iteration joint sparsity is used to improve efficiency. Experimental results not only validate that the proposed method outperforms the existing off-grid imaging methods in terms of accuracy and resolution, but have compared the root mean square error with corresponding BCRB, proving effectiveness of the proposed method.
Highlights
In the last few years, three-dimensional (3D) radar imaging systems and algorithms have received significant attention among researchers worldwide
Starting from the purpose of finding a 3D imaging method with high accuracy for maneuvering off-grid air target, we propose a novel algorithm based on improved sparse Bayesian learning to overcome the aforementioned problems
In order to solve the problems of off-grid target 3D imaging, a novel sparse Bayesian learning-based algorithm using sparse antenna array is proposed in this paper
Summary
In the last few years, three-dimensional (3D) radar imaging systems and algorithms have received significant attention among researchers worldwide. Multiple-input–multiple-output (MIMO) radar system has drawn much attention and shows its potential in 3D imaging in the last decade [3]. For one thing, it can overcome the problem of limited number of antennas and limited aperture size. In the last few years, compressed sensing (CS) has been widely used in sparse signal recovery with fewer samples [8] It has shown great advantage in radar imaging due to its super-resolution ability. It is proven that CS-based imaging algorithms provide a better resolution enhancement effect than the RELAX algorithm [9] It can exploit the sparsity of signal and reconstruct the signal from limited samples with high probability. Sparse recovery algorithm with high accuracy is of fundamental importance in MIMO 3D imaging
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