机载相控阵雷达时常会面对山地、丘陵、海陆交界、城市、高大建筑以及塔架等复杂多样的非均匀杂波环境，使得传统STAP方法所需要的均匀训练样本数量难以得到保证，杂波抑制性能严重下降。此外，复杂多变的杂波环境也对当前系统的实时处理能力提出了严峻挑战。因此，适用于复杂环境快速稳健的杂波抑制研究是目前机载相控阵雷达领域亟待解决的关键问题。本项目基于机载相控阵雷达杂波谱和杂波自由度稀疏分布特性及正定Hermitian空时协方差矩阵的阶数分块递推特性，分别提出基于杂波空时谱稀疏分布特性的快速稳健STAP算法、基于杂波自由度稀疏特性的快速子空间迭代跟踪的STAP算法及基于阶数递推的快速STAP自适应权值计算方法，并利用实测数据对所提出的技术和方法进行验证，为机载相控阵雷达的实际应用提供重要的参考。

Airborne phased array radar would often confront complex nonhomogeneous environment, such as mountainous region, hills, land, sea border, city, huge building and tower, which makes the number of homogeneous training samples insufficient, so that the clutter suppression of traditional STAP methods deteriorates significantly. Meanwhile, the fast varying clutter environment brings out sever challenge to the current real-time processing. Therefore, it is urgent and necessary to develop fast and robust STAP methods for the practical complex environment. In this project, based on the sparse distributed property of clutter spatial-temporal spectrum, a robust and fast iterative sparse recovery method for STAP is proposed, based on the sparse property of the degrees of freedom (DoFs) of clutter, a fast STAP method based on projection approximation subspace tracking with the sparse constraint is proposed and based on the order recursion property of the Hermitian space-time covariance matrix, a fast recursive weight computation method for STAP is proposed. The effectiveness of the proposed techniques and methods will be verified by actual airborne phased array radar data. The related research in this project could provide the valuable reference for actual airborne phased array radar system.