频控阵MIMO雷达中基于稀疏迭代的多维信息联合估计方法

Multidimensional Parameter Estimation Method Based on Sparse Iteration in FDA-MIMO Radar

传统MIMO雷达为了获得准确的距离信息不仅需要引入转移矩阵,而且需要积累更多的快拍数。针对该问题,该文提出了基于稀疏迭代的频控阵MIMO雷达多维信息(距离、角度和幅度)估计方法,该方法利用频控阵MIMO的发射导向矢量不仅与目标的方位角有关,同时也与目标的距离有关这一特性,通过优化加权lq范数(0<q≤1)的目标函数,采用稀疏迭代优化获取目标的距离、角度和幅度信息。该方法仅需单次快拍,并在每次迭代中求解到了目标的多维信息,故不需借助优化工具获得目标信息。仿真显示,相比DAS,IAA和IAA-R,该方法在有效减少快拍数和处理数据的同时,获得了高分辨率的目标距离、角度和幅度估计性能。

To accurately identify the range of each target, traditional Multiple-Input Multiple-Output（MIMO）radar techniques not only require designing a shift matrix to describe different range bins but also a large number of snapshots. To alleviate this problem, a multidimensional parameter estimation method based on sparse iteration is proposed for a MIMO radar with Frequency Diverse Array（FDA）. The FDA-MIMO radar uses small frequency increments across the array elements, and its transmit steering vector is a function of both range and angle. On the basis of the feature of the FDA-MIMO radar, we consider a weighted lq（0〈q ≤1）minimization problem that is solved using a sparse iterative algorithm. Finally, the target parameters（the amplitude, range, and angle） are obtained using a single snapshot. Moreover, numerical simulations are used to demonstrate the superior performance of the proposed method compared with those of DAS, IAA, and IAA-R.