Abstract In high dynamic environment, the output performance of the space-time adaptive processing (STAP) beamformer can be degraded dramatically since directions of arrival (DOAs) corresponding to interferences change rapidly. A novel robust STAP beamforming algorithm is proposed for global navigation satellite system (GNSS) receivers to broaden the nulls towards interferences and meanwhile fight against the steering vector (SV) mismatch caused by DOA estimation errors. Firstly, the received signal model in high dynamic environment is established for the STAP architecture. Then, the spatial-temporal interference plus noise covariance (INC) matrix is obtained by reconstructing the interference covariance matrix and noise covariance matrix respectively to avoid DOA estimation of GNSS signals, which can also remove the signal of interest (SOI) component from the sample covariance matrix (SCM). Furthermore, the spatial-temporal spectrum estimates around interferences are respectively reset to be the same as those of interferences, due to which the nulls towards interferences in the space domain can be widened in high dynamic environment. Finally, the constraints to solve weight vector is specially designed for GNSS signals. Simulation results show that the proposed algorithm can broaden the nulls effectively and also has a better output carrier-to-noise (C/No) ratio performance than the other involving algorithms.

中文翻译：

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用于高动态环境下 GNSS 接收机的鲁棒 STAP 波束成形算法

摘要 在高动态环境下，时空自适应处理（STAP）波束形成器的输出性能会因干扰对应的到达方向（DOA）快速变化而急剧下降。为全球导航卫星系统 (GNSS) 接收器提出了一种新颖的鲁棒 STAP 波束成形算法，以将零点扩大到干扰，同时对抗由 DOA 估计误差引起的导向矢量 (SV) 失配。首先，针对STAP架构建立了高动态环境下的接收信号模型。然后通过分别重构干扰协方差矩阵和噪声协方差矩阵得到时空干扰加噪声协方差矩阵，避免GNSS信号的DOA估计，它还可以从样本协方差矩阵 (SCM) 中去除感兴趣的信号 (SOI) 分量。此外，干扰周围的时空频谱估计分别被重置为与干扰的时空频谱估计相同，因此在高动态环境中可以扩大空间域中干扰的零点。最后，求解权向量的约束是专门为 GNSS 信号设计的。仿真结果表明，所提出的算法能有效地拓宽零点，并且比其他涉及的算法具有更好的输出载噪比（C/No）性能。因此，在高动态环境中可以扩大空间域中干扰的零点。最后，求解权向量的约束是专门为 GNSS 信号设计的。仿真结果表明，所提出的算法能有效地拓宽零点，并且比其他涉及的算法具有更好的输出载噪比（C/No）性能。因此，在高动态环境中可以扩大空间域中干扰的零点。最后，求解权向量的约束是专门为 GNSS 信号设计的。仿真结果表明，所提出的算法能有效地拓宽零点，并且比其他涉及的算法具有更好的输出载噪比（C/No）性能。