Two-Dimensional DOA Estimation Based on Separable Observation Model Utilizing Weighted L1-Norm Penalty and Bayesian Compressive Sensing Strategy
- Resource Type
- Conference
- Authors
- Wei, Ruiqi; Wang, Qianli; Zhao, Zhiqin
- Source
- 2017 4th International Conference on Information Science and Control Engineering (ICISCE) ICISCE Information Science and Control Engineering (ICISCE), 2017 4th International Conference on. :1764-1768 Jul, 2017
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Direction-of-arrival estimation
Estimation
Compressed sensing
Bayes methods
Sparse matrices
Azimuth
Manganese
weighted L1-norm penalty
2D-DOA
multitask Bayesian compressive sensing
separable observation model
- Language
A novel approach for two-dimensional direction of arrival (2D-DOA) estimation based on separable observation model utilizing weighted L1-norm penalty and multitask Bayesian compressive sensing is proposed. Unlike previous DOA estimation methods based on the separable observation model, the proposed method has a better performance at the circumstance of low SNRs and small scale planar array. This approach employs weighted L1-norm penalty method to increase the reconstruction accuracy of elevations firstly. Then multitask Bayesian compressive sensing method is applied to reconstruct the sparse matrix. As the Bayesian compressive sensing method doesn't need the residual information produced during reconstructing the auxiliary variable, thus avoiding selecting regularization parameters, giving rise to better robust performance of the method. Simulation results validate the effectiveness of the proposed method.