Off-Grid Direction of Arrival Estimation Using Sparse Bayesian Inference

By Zai Yang, Lihua Xie, Cisheng Zhang. IEEE Transactions on Signal Processing, 2011

873 citations recorded by the source corpus.

Direction of arrival (DOA) estimation is a classical problem in signal processing with many practical applications. Its research has recently been advanced owing to the development of methods based on sparse signal reconstruction. While these methods have shown advantages over conventional ones, there are still difficulties in practical situations where true DOAs are not on the discretized sampling grid. To deal with such an off-grid DOA estimation problem, this paper studies an off-grid model that takes into account effects of the off-grid DOAs and has a smaller modeling error. An iterative algorithm is developed based on the off-grid model from a Bayesian perspective while joint sparsity among different snapshots is exploited by assuming a Laplace prior for signals at all snapshots. The new approach applies to both single snapshot and multi-snapshot cases. Numerical simulations show that the proposed algorithm has improved accuracy in terms of mean squared estimation error. The algorithm can maintain high estimation accuracy even under a very coarse sampling grid.

Subfields: Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6b45691d31b09f19249e8fd5beb9359e4e024411

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Paolo Rocca
2. Giacomo Oliveri
3. Andrea Massa
4. P. Stoica
5. Jian Li
6. Lihua Xie
7. Zai Yang
8. Jinhong Yuan

In Signal Processing

1. Jian Li
2. P. Stoica
3. Lihua Xie
4. Zai Yang
5. Paolo Rocca
6. Andrea Massa
7. Giacomo Oliveri
8. Jinhong Yuan

Citations in the corpus

• Reconfigurable Intelligent Surfaces for Localization: Position and Orientation Error Bounds (2020, background): “The compressive sensing based estimators have the advantage that the angles can be recovered even from a single snapshot of y , while the performance can be further enhanced by considering multiple measurement vectors (i.e., several snapshots) [55]– [58].”
• On Gridless Sparse Methods for Line Spectral Estimation From Complete and Incomplete Data (2014, positive): “For SPICE and SPGL1, the MSEs without the framework are lower bounded by (the horizontal black dashed lines) in expectation since the best frequency estimate is the nearest grid point [19].”
• Compressive Sensing in Electromagnetics - A Review (2015, methodology): “In addition, BCS approaches [9] have been investigated for solving the perturbed CS problem [68].”
• Sparse Methods for Direction-of-Arrival Estimation (2016, methodology): “3 Sparse Bayesian Learning A systematic approach to off-grid DOA estimation, called off-grid sparse Bayesian inference (OGSBI), was proposed in [101] within the framework of SBL in the multiple snapshot case.”
• Deep Learning for Super-Resolution Channel Estimation and DOA Estimation Based Massive MIMO System (2018, methodology): “Fig.7 exhibits the MSE performance of the DOA estimation against the SNR of the proposed DL based scheme, SBEM scheme [17], PA channel tracking scheme [21], ADMA user scheduling scheme [20], and compressed sensing scheme [45], respectively.”
• Off-grid Channel Estimation with Sparse Bayesian Learning for OTFS Systems (2021, methodology): “In our proposed Algorithms 1 and 2, we set = 10−3, ρ = 10−2, and c = d = 10−4 [16].”