# Study of Robust Direction Finding Based on Joint Sparse Representation

arXiv: https://arxiv.org/abs/2405.16765 · HTML: https://mlsanity.com/paper/2405.16765/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 27 May 2024.

## Builds on (most cited first, 4 of 4)

- [Direction Finding Algorithms with Joint Iterative Subspace Optimization](https://mlsanity.com/paper/1402.2710/) (2014)
- [Tensor Recovery Based on A Novel Non-convex Function Minimax Logarithmic Concave Penalty Function](https://mlsanity.com/paper/2206.13506/) (2022)
- [Multichannel sparse recovery of complex-valued signals using Huber's criterion](https://mlsanity.com/paper/1504.04184/) (2015)
- [Off-Grid Direction-of-Arrival Estimation Using Second-Order Taylor Approximation](https://mlsanity.com/paper/2112.05487/) (2021)

## Abstract (arXiv)

Standard Direction of Arrival (DOA) estimation methods are typically derived based on the Gaussian noise assumption, making them highly sensitive to outliers. Therefore, in the presence of impulsive noise, the performance of these methods may significantly deteriorate. In this paper, we model impulsive noise as Gaussian noise mixed with sparse outliers. By exploiting their statistical differences, we propose a novel DOA estimation method based on sparse signal recovery (SSR). Furthermore, to address the issue of grid mismatch, we utilize an alternating optimization approach that relies on the estimated outlier matrix and the on-grid DOA estimates to obtain the off-grid DOA estimates. Simulation results demonstrate that the proposed method exhibits robustness against large outliers.