Sparse Frequency Diverse MIMO Radar Imaging for Off-Grid Target Based on Adaptive Iterative MAP

Remote Sensing

Xuezhi He¹  Changchang Liu²  Bo Liu² 

[1] Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, Anhui, China;

关键词: frequency diverse MIMO radar imaging;    sparse recovery;    adaptive calibration;    off-grid;    maximum a posteriori (MAP);   

DOI  :  10.3390/rs5020631

来源: mdpi

【 摘 要 】

The frequency diverse multiple-input-multiple-output (FD-MIMO) radar synthesizes a wideband waveform by transmitting and receiving multiple frequency signals simultaneously. For FD-MIMO radar imaging, conventional imaging methods based on Matched Filter (MF) cannot enjoy good imaging performance owing to the few and incomplete wavenumber-domain coverage. Higher resolution and better imaging performance can be obtained by exploiting the sparsity of the target. However, good sparse recovery performance is based on the assumption that the scatterers of the target are positioned at the pre-discretized grid locations; otherwise, the performance would significantly degrade. Here, we propose a novel approach of sparse adaptive calibration recovery via iterative maximum a posteriori (SACR-iMAP) for the general off-grid FD-MIMO radar imaging. SACR-iMAP contains three loop stages: sparse recovery, off-grid errors calibration and parameter update. The convergence and the initialization of the method are also discussed. Numerical simulations are carried out to verify the effectiveness of the proposed method.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.