Research Article
2D DOA Estimation Based on Modified Compressed Sensing Algorithm
@INPROCEEDINGS{10.1007/978-3-030-69066-3_2, author={Chang Fu and Jun Ma}, title={2D DOA Estimation Based on Modified Compressed Sensing Algorithm}, proceedings={Artificial Intelligence for Communications and Networks. Second EAI International Conference, AICON 2020, Virtual Event, December 19-20, 2020, Proceedings}, proceedings_a={AICON}, year={2021}, month={7}, keywords={2D DOA estimation Compressed sensing SVD}, doi={10.1007/978-3-030-69066-3_2} }- Chang Fu
Jun Ma
Year: 2021
2D DOA Estimation Based on Modified Compressed Sensing Algorithm
AICON
Springer
DOI: 10.1007/978-3-030-69066-3_2
Abstract
In order to realize high-precision DOA tracking in space, researches on two-dimensional DOA estimation have been conducted in recent years. The existing algorithms often need large snapshots for estimation accuracy, going against the fast solution. Considering the low sensitivity of DOA estimation algorithm based on compressed sensing theory to the number of snapshots and the correct estimation with less sampling data, a modified two-dimensional multitask compressed sensing algorithm based on SVD decomposition is proposed in this paper. This algorithm makes up for the drawbacks of existing compressed sensing algorithms in dealing with multi snapshot problem and reduces the unnecessary calculation. Simulation results show that the proposed algorithm can solve the off-grid problem in compressed sensing, and has better estimation performance than other algorithms under the condition of low SNR and few snapshots.