Imaging of moving target for distributed MIMO radar using improved SBL technique
Signal Processing, Communications and Computing（2014）
摘要
The distributed multiple input multiple output (MIMO) radar has the potential to achieve high resolution. But when the target is moving, the imaging result will be blurred if we don't consider the effect of the motion. To solve this problem, the velocity of the target will be estimated along with target recovery in a loop iteration process. Furthermore, by utilizing the sparsity of the scatterers, we use compressive sensing (CS) method to obtain better performance. The improved Sparse Bayesian Learning (SBL) technique is used in this paper for target recovery and velocity estimation. The effectiveness of the proposed sparse recovery approach based on SBL (SRA-SBL) is confirmed by several experimental results.
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关键词
bayes methods,mimo radar,compressed sensing,image motion analysis,inference mechanisms,learning (artificial intelligence),radar computing,radar imaging,compressive sensing method,distributed mimo radar,distributed multiple input multiple output radar,improved sbl technique,moving target imaging,sparse bayesian learning technique,sparse recovery,target recovery,velocity estimation,sparse bayesian learning (sbl)
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