# Analysis of Partially-Calibrated Sparse Subarrays for Direction Finding with Extended Degrees of Freedom

arXiv: https://arxiv.org/abs/2408.03236 · HTML: https://mlsanity.com/paper/2408.03236/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 47 references that are in the map.
- First posted on arXiv 6 Aug 2024.

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

- [Design of Robust Adaptive Beamforming Algorithms Based on Low-Rank and Cross-Correlation Techniques](https://mlsanity.com/paper/1606.01313/) (2016)
- [Direction Finding Algorithms with Joint Iterative Subspace Optimization](https://mlsanity.com/paper/1402.2710/) (2014)
- [Sparsity-Aware Adaptive Algorithms Based on Alternating Optimization with Shrinkage](https://mlsanity.com/paper/1401.0463/) (2014)
- [Direction of Arrival Estimation for Non-Coherent Sub-Arrays via Joint Sparse and Low-Rank Signal Recovery](https://mlsanity.com/paper/2011.02083/) (2020)

## Abstract (arXiv)

This paper investigates the problem of direction-of-arrival (DOA) estimation using multiple partially-calibrated sparse subarrays. In particular, we present the Generalized Coarray Multiple Signal Classification (GCA-MUSIC) DOA estimation algorithm to scenarios with partially-calibrated sparse subarrays. The proposed GCA-MUSIC algorithm exploits the difference coarray for each subarray, followed by a specific pseudo-spectrum merging rule that is based on the intersection of the signal subspaces associated to each subarray. This rule assumes that there is no a priori knowledge about the cross-covariance between subarrays. In that way, only the second-order statistics of each subarray are used to estimate the directions with increased degrees of freedom, i.e., the estimation procedure preserves the coarray Multiple Signal Classification and sparse arrays properties to estimate more sources than the number of physical sensors in each subarray. Numerical simulations show that the proposed GCA-MUSIC has better performance than other similar strategies.