Abstract
Aiming at the problem of scattering centers resolving and angular positions estimation of spatially extended targets, a high-resolution and high-accuracy angle estimation method based on multi-task group sparse model and collocated MIMO radar is proposed, which is helpful to obtain the structure information of targets and improve the success rate of target recognition. Characterized by sparse and clustered distribution in space, angular positions estimation of multiple closely-spaced and correlated point scattering targets belonging to a spatially extended target can be modeled as a multi-task group sparse problem and can be solved by multi-task group sparse recovery. To overcome the sparse recovery performance degradation caused by the high correlation in group sparse solution matrix and to improve the accuracy and robustness of angle estimation, a complex spatiotemporal sparse Bayesian learning (CST-SBL) algorithm which exploits spatiotemporal correlation structures of the solution matrix is proposed to reconstruct angular positions. Compared with previous work, the proposed approach achieves high-resolution and high-accuracy estimation performance, especially in cases of low SNR and few snapshots. The theoretical analysis and simulation results validate the effectiveness of the proposed technique.
Highlights
High resolution radar imaging is playing an increasingly important role in many application scenarios, such as through-the-wall radar imaging [1], synthetic aperture radar (SAR) imaging [2] and automotive radar for highly automated driving [3], where a high-resolution cross-range profile is desired in order to distinguish targets better
multiple-input multiple-output (MIMO) RADAR CONFIGURATION For illustrative purpose, we consider a collocated MIMO radar equipped with NTx = 4 Tx elements and NRx = 8 Rx elements, where the Tx array is divided into two parts placed at both sides of the Rx array with 7.5λ spacing and each part having two elements with 0.5λ element spacing, while the Rx array is a uniform linear array (ULA) with λ element spacing
The collocated MIMO radar works in a time division multiplexing (TDM) mode shown as Fig. 2
Summary
High resolution radar imaging is playing an increasingly important role in many application scenarios, such as through-the-wall radar imaging [1], synthetic aperture radar (SAR) imaging [2] and automotive radar for highly automated driving [3], where a high-resolution cross-range profile is desired in order to distinguish targets better. In [23], a structural SBL algorithm which exploits intra-signal correlation was proposed for the angle estimation of spatially distributed targets, and achieved more accurate estimation performance than the traditional MUSIC algorithm and other sparse recovery algorithms. A framework of angular positions estimation based on multi-task group sparse model and collocated MIMO radar is proposed to address this problem. Angular positions estimation of multiple closely-spaced and correlated point scattering targets is modeled as a group sparse recovery problem. A complex spatiotemporal sparse Bayesian learning (CST-SBL) algorithm with no need of the abovementioned priori knowledge is proposed to recovery the group sparse matrix S which indicates the angular positions of closely-spaced and correlated point scattering targets of spatially extended targets. The resulting algorithm alternates the estimation between the two models until convergence
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