For airborne-phased array radar systems, space-time adaptive processing (STAP) is supposed to be a crucial technique for improving target detection performance in the strong clutter background. However, practical application environments are always heterogeneous and have offered a severe challenge to the implementation of STAP. To address this reality, a data-dependent reduced dimension STAP approach based on sparse recovery (SR) is proposed in this paper. To take a full account of the heterogeneous environments, we consider the extremely heterogeneous case in which only one single snapshot is available. There is no doubt that, compared with the clutter covariance matrix (CCM) calculated directly by a single snapshot, the SR technique can provide a more accurate estimate. However, we should come to realize that this estimation is not accurate enough to adaptive processing according to the presented simulation results in lots of literature, and it has been demonstrated that the performance of traditional SR-based STAP degrades dramatically when only a snapshot is available. In the proposed approach, the CCM estimated by SR technique is utilized to design the reduced dimension transformation matrix rather than to calculate adaptive weights as the traditional SR-based STAP. The relatively accurate CCM can provide better support for the design of reduced dimension transformation matrix. From the simulation results, the proposed approach can achieve great performance of clutter suppression and target detection with only a single snapshot compared with several typical STAP algorithms. It is worthwhile pointing out that the proposed approach can also be applied when multiple snapshots are available and the performance improves with increasing number of available snapshots.

中文翻译：

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异构杂波环境下基于稀疏恢复的降维STAP

对于机载相控阵雷达系统，空时自适应处理（STAP）被认为是提高强杂波背景下目标检测性能的关键技术。然而，实际应用环境总是异构的，给STAP的实现提出了严峻的挑战。为了解决这一现实，本文提出了一种基于稀疏恢复（SR）的数据相关降维 STAP 方法。为了充分考虑异构环境，我们考虑只有一个快照可用的极端异构情况。毫无疑问，与单次快照直接计算的杂波协方差矩阵（CCM）相比，SR技术可以提供更准确的估计。然而，我们应该意识到，根据大量文献中呈现的模拟结果，这种估计不足以进行自适应处理，并且已经证明，当只有快照可用时，传统的基于 SR 的 STAP 的性能会急剧下降。在所提出的方法中，利用 SR 技术估计的 CCM 来设计降维变换矩阵，而不是像传统的基于 SR 的 STAP 那样计算自适应权重。相对准确的CCM可以为降维变换矩阵的设计提供更好的支持。从仿真结果来看，与几种典型的STAP算法相比，所提出的方法仅使用单个快照即可实现良好的杂波抑制和目标检测性能。