稀疏处理主要研究如何利用低维数据实现高维稀疏信号的准确重建。基于该特性,已有较多学者将其应用于ISAR成像中用以减少数据量,改善成像质量。文中首先从ISAR回波数据出发,建立了基于稀疏处理的ISAR成像模型,并给出实验处理结果;然后,针对稀疏ISAR成像中存在的对目标和环境的自适应能力不强、工作效率不高等问题,提出将稀疏ISAR成像与认知雷达相结合,构建了认知稀疏ISAR成像框图,并给出了两种认知稀疏ISAR成像策略,能够有效减少成像所需数据量,提高雷达利用效率;最后,利用实验数据验证了所提模型的有效性。

Sparse processing has been utilized to reconstruct the high-dimensional sparse singal with low-dimensional data. Based on this property, sparse processing has been applied to ISAR imaging to reduce the data amount and improve imaging quality. Firstly, after analyzing the echo signal model, an ISAR imaging method based on sparse processing is introduced, and the experimental results are put forward. Then aiming at solving the low adaptive adjustment performance for target, environment and low capacity of working, sparse ISAR imaging and cognitive radar are combined, two kinds of cognitive sparse ISAR imaging schemes are presented to the situations of single target and multiple targets correspondently. Finally, the experimental data is utilized to prove the effectiveness.

TN957.51

中国博士后科学基金资助项目