Direction of Arrival Estimation Based on Simplified Dictionary Matching Pursuit Algorithm with Rotational Invariance

Positioning based on direction of arrival (DOA) estimation is widely used in Internet of Things (IoT). For indoor positioning scenarios, this paper proposes a simplified dictionary orthogonal matching pursuit algorithm based on rotational in-variance (SDOMPRI). The algorithm takes advantage of both rotational invariant subspace and orthogonal matching pursuit (OMP) algorithm to obtain high angle estimation accuracy. It exploits the ability of OMP to improves the performance of anti-interference through sparse signal reconstruction, and the ability of rotational invariant subspace to break the Rayleigh limit of OMP, improve the angle resolution and reduce the computational complexity. The simulation results show that SDOMPRI has obvious advantages in the performance comparison of angular separation accuracy, angle estimation accuracy under different SNR, and error cumulative distribution. For the accuracy under different snapshots, SDOMPRI performs well and its root mean square error (RMSE) decreases more than 45% compared with other algorithms when the difference between adjacent sources is large. The time consumption is also lower than other algorithms. It meets the requirements of high accuracy, anti-interference and low latency in positioning scenarios.

基于波达方向（DOA）估计的定位在物联网（IoT）中应用广泛。针对室内定位场景，本文提出了一种基于旋转不变性的简化字典正交匹配追踪算法（SDOMPRI）。该算法结合了旋转不变子空间和正交匹配追踪（OMP）算法的优势，以获得较高的角度估计精度。它利用OMP通过稀疏信号重构提升抗干扰性能的能力，以及旋转不变子空间突破OMP瑞利限、提高角度分辨率并降低计算复杂度的能力。仿真结果表明，SDOMPRI在角度分离精度、不同信噪比下的角度估计精度以及误差累积分布等性能对比中具有明显优势。对于不同快拍下的精度，SDOMPRI表现良好，当相邻信源差异较大时，其均方根误差（RMSE）相较于其他算法降低超过45%。其耗时也低于其他算法。该算法满足定位场景中高精度、抗干扰和低延迟的要求。