# Joint optimization of system design and reconstruction in MIMO radar imaging

arXiv: https://arxiv.org/abs/2110.03218 · HTML: https://mlsanity.com/paper/2110.03218/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 7 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [fastMRI: An Open Dataset and Benchmarks for Accelerated MRI](https://mlsanity.com/paper/1811.08839/) (2018)
- [Reparameterizable Subset Sampling via Continuous Relaxations](https://mlsanity.com/paper/1901.10517/) (2019)
- [PILOT: Physics-Informed Learned Optimized Trajectories for Accelerated MRI](https://mlsanity.com/paper/1909.05773/) (2019)
- [Joint learning of cartesian undersampling and reconstruction for accelerated MRI](https://mlsanity.com/paper/1905.09324/) (2019)
- [Generating High Quality Visible Images from SAR Images Using CNNs](https://mlsanity.com/paper/1802.10036/) (2018)
- [Sparse Array Selection Across Arbitrary Sensor Geometries with Deep Transfer Learning](https://mlsanity.com/paper/2004.11637/) (2020)
- [High Resolution Millimeter Wave Imaging For Self-Driving Cars](https://mlsanity.com/paper/1912.09579/) (2019)
- [Learning beamforming in ultrasound imaging](https://mlsanity.com/paper/1812.08043/) (2018)
- [Towards learned optimal q-space sampling in diffusion MRI](https://mlsanity.com/paper/2009.03008/) (2020)

## Built on by (most cited first, 3 of 3)

- [Efficient Physics-Based Learned Reconstruction Methods for Real-Time 3D Near-Field MIMO Radar Imaging](https://mlsanity.com/paper/2312.16959/) (2023)
- [Floor Map Reconstruction Through Radio Sensing and Learning By a Large Intelligent Surface](https://mlsanity.com/paper/2206.10750/) (2022)
- [DeeperRadar: End-to-End MIMO Radar Design and Multi-Modal Fusion for Autonomous Vehicle Perception](https://mlsanity.com/paper/2607.17351/) (2026)

## Abstract (arXiv)

Multiple-input multiple-output (MIMO) radar is one of the leading depth sensing modalities. However, the usage of multiple receive channels lead to relative high costs and prevent the penetration of MIMOs in many areas such as the automotive industry. Over the last years, few studies concentrated on designing reduced measurement schemes and image reconstruction schemes for MIMO radars, however these problems have been so far addressed separately. On the other hand, recent works in optical computational imaging have demonstrated growing success of simultaneous learning-based design of the acquisition and reconstruction schemes, manifesting significant improvement in the reconstruction quality. Inspired by these successes, in this work, we propose to learn MIMO acquisition parameters in the form of receive (Rx) antenna elements locations jointly with an image neural-network based reconstruction. To this end, we propose an algorithm for training the combined acquisition-reconstruction pipeline end-to-end in a differentiable way. We demonstrate the significance of using our learned acquisition parameters with and without the neural-network reconstruction.