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 invariance (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.