# Sparse Array Selection Across Arbitrary Sensor Geometries with Deep Transfer Learning

arXiv: https://arxiv.org/abs/2004.11637 · HTML: https://mlsanity.com/paper/2004.11637/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [Terahertz-Band Joint Ultra-Massive MIMO Radar-Communications: Model-Based and Model-Free Hybrid Beamforming](https://mlsanity.com/paper/2103.00328/) (2021)
- [Joint optimization of system design and reconstruction in MIMO radar imaging](https://mlsanity.com/paper/2110.03218/) (2021)
- [DeepAoANet: Learning Angle of Arrival from Software Defined Radios with Deep Neural Networks](https://mlsanity.com/paper/2112.00695/) (2021)
- [Sparse Array Design for Direction Finding using Deep Learning](https://mlsanity.com/paper/2308.04615/) (2023)

## Abstract (arXiv)

Sparse sensor array selection arises in many engineering applications, where it is imperative to obtain maximum spatial resolution from a limited number of array elements. Recent research shows that computational complexity of array selection is reduced by replacing the conventional optimization and greedy search methods with a deep learning network. However, in practice, sufficient and well-calibrated labeled training data are unavailable and, more so, for arbitrary array configurations. To address this, we adopt a deep transfer learning (TL) approach, wherein we train a deep convolutional neural network (CNN) with data of a source sensor array for which calibrated data are readily available and reuse this pre-trained CNN for a different, data-insufficient target array geometry to perform sparse array selection. Numerical experiments with uniform rectangular and circular arrays demonstrate enhanced performance of TL-CNN on the target model than the CNN trained with insufficient data from the same model. In particular, our TL framework provides approximately 20% higher sensor selection accuracy and 10% improvement in the direction-of-arrival estimation error.