Multiple-input multiple-output (MIMO) radar imaging is a new technique to obtain the radar image of aerospace targets. Orthogonal waveform design is one of the important issues for MIMO radar imaging. However, the fully orthogonal waveforms in the same frequency and with the arbitrary time delay do not exist in practice. Thus, the imaging result using nonorthogonal waveforms based on matched filtering (MF) method is usually unsatisfactory if further processing like digital beam forming (DBF) is not used. Sparse recovery (SR) method is possible to restrain the mutual interference of nonorthogonal waveforms by exploiting the sparsity of targets and improve the imaging quality. In this article, waveform design issue in SR-based MIMO imaging method is studied. The difference in the designs of waveforms in MF method and SR method is discussed. Based on requirements analysis, a comprehensive optimization model is built for waveform design and the existing cycle algorithm (CA) is modified to solve the model. Considering the fact that the target scene is always changing, waveforms should be adjusted along with the dynamic scene. Therefore, an adaptive waveform optimization method is further proposed based on the cognition of target scene. The dimension of SR model is reduced and the waveforms are optimized according to the cognitive target length. Moreover, based on the reconstructed target range profiles, transmitting waveforms together with recovery algorithm are further optimized to match the target better. Simulation results show that the waveforms after optimization are better than the nonoptimized waveforms and the proposed adaptive optimization method is valid and robust for the dynamic target scene.

中文翻译：

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基于稀疏恢复的MIMO雷达成像自适应波形优化

多输入多输出（MIMO）雷达成像是一种获取航天目标雷达图像的新技术。正交波形设计是MIMO雷达成像的重要问题之一。然而，在实际中并不存在相同频率且具有任意时延的完全正交波形。因此，如果不使用数字波束形成（DBF）等进一步处理，基于匹配滤波（MF）方法使用非正交波形的成像结果通常不能令人满意。稀疏恢复(SR)方法可以利用目标的稀疏性来抑制非正交波形的相互干扰，提高成像质量。本文研究了基于SR的MIMO成像方法中的波形设计问题。讨论了MF方法和SR方法在波形设计上的差异。在需求分析的基础上，建立了波形设计的综合优化模型，并修改了现有的循环算法（CA）对模型进行求解。考虑到目标场景一直在变化，波形应该随着动态场景的变化而调整。因此，进一步提出了一种基于目标场景认知的自适应波形优化方法。SR模型降维，根据认知目标长度优化波形。此外，基于重建的目标距离剖面，进一步优化发射波形和恢复算法，以更好地匹配目标。