IEEE Transactions on Signal Processing· 2012Q1
Off-Grid Direction of Arrival Estimation Using Sparse Bayesian Inference
- 912citations
- Q1SCImago
- 2012year
Short summary
A novel iterative Bayesian algorithm accurately estimates the direction of arrival (DOA) of signals even when the true DOAs do not align with the discrete sampling grid, outperforming existing methods in mean squared error.
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Key points
- Developed an iterative Bayesian algorithm for off-grid DOA estimation.
- The algorithm uses an off-grid model with smaller modeling error.
- Joint sparsity among snapshots is exploited using a Laplace prior.
- Achieves improved accuracy in mean squared estimation error compared to existing methods.
- Maintains high accuracy even with very coarse sampling grids.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. IEEE Transactions on Signal Processing, 2012 · DOI ↗
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Field: Signal Processing
Signal ProcessingComputer Science