Zhang Shuguang, Wang Tong, Liu Cheng, Wang Degen
National Lab of Radar Signal Processing, Xidian University, Xi'an 710071, China.
Sensors (Basel). 2022 Jul 22;22(15):5479. doi: 10.3390/s22155479.
Space-time adaptive processing (STAP) is an effective technology in clutter suppression and moving target detection for airborne radar. Because airborne radar moves at a constant acceleration, and there is a lack of independent and identically distributed (IID) training samples caused by the heterogeneous environment, using the conventional STAP methods directly cannot ensure a good performance. To eliminate these effects and improve the performance of clutter suppression, a STAP method based on a sparse Bayesian learning (SBL) framework for uniform acceleration radar is proposed here. This paper introduces the signal model of the uniform acceleration radar. To promote the sparsity, a generalized double Pareto (GDP) prior is introduced into our method, and the estimation of hyper parameters via expectation maximization (EM) is given. The effectiveness of the proposed method is demonstrated by simulations.
空时自适应处理（STAP）是机载雷达杂波抑制和动目标检测中的一项有效技术。由于机载雷达以恒定加速度运动，且异构环境导致缺乏独立同分布（IID）训练样本，直接使用传统的STAP方法无法确保良好性能。为消除这些影响并提高杂波抑制性能，本文提出一种基于稀疏贝叶斯学习（SBL）框架的均匀加速度雷达STAP方法。本文介绍了均匀加速度雷达的信号模型。为提高稀疏性，在我们的方法中引入了广义双帕累托（GDP）先验，并给出了通过期望最大化（EM）对超参数的估计。仿真结果验证了所提方法的有效性。
